Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.1 KiB
Average record size in memory72.2 B

Variable types

Numeric8
Categorical1

Alerts

Age is highly overall correlated with PregnanciesHigh correlation
BMI is highly overall correlated with SkinThicknessHigh correlation
Pregnancies is highly overall correlated with AgeHigh correlation
SkinThickness is highly overall correlated with BMIHigh correlation

Reproduction

Analysis started2025-06-20 20:01:59.889770
Analysis finished2025-06-20 20:02:04.466055
Duration4.58 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Pregnancies
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4946728
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-06-20T23:02:04.502838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4.4946728
Q36
95-th percentile10
Maximum17
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9753948
Coefficient of variation (CV)0.66198253
Kurtosis0.66677292
Mean4.4946728
Median Absolute Deviation (MAD)2.4946728
Skewness0.95710582
Sum3451.9087
Variance8.8529744
MonotonicityNot monotonic
2025-06-20T23:02:04.560375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 135
17.6%
4.494672755 111
14.5%
2 103
13.4%
3 75
9.8%
4 68
8.9%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
Other values (7) 58
7.6%
ValueCountFrequency (%)
1 135
17.6%
2 103
13.4%
3 75
9.8%
4 68
8.9%
4.494672755 111
14.5%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
ValueCountFrequency (%)
17 1
 
0.1%
15 1
 
0.1%
14 2
 
0.3%
13 10
 
1.3%
12 9
 
1.2%
11 11
 
1.4%
10 24
3.1%
9 28
3.6%
8 38
4.9%
7 45
5.9%

Glucose
Real number (ℝ)

Distinct136
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.68676
Minimum44
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-06-20T23:02:04.630424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile80
Q199.75
median117
Q3140.25
95-th percentile181
Maximum199
Range155
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation30.435949
Coefficient of variation (CV)0.25011717
Kurtosis-0.2591586
Mean121.68676
Median Absolute Deviation (MAD)20
Skewness0.53271866
Sum93455.434
Variance926.34698
MonotonicityNot monotonic
2025-06-20T23:02:04.705948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 17
 
2.2%
100 17
 
2.2%
111 14
 
1.8%
125 14
 
1.8%
129 14
 
1.8%
106 14
 
1.8%
102 13
 
1.7%
105 13
 
1.7%
112 13
 
1.7%
95 13
 
1.7%
Other values (126) 626
81.5%
ValueCountFrequency (%)
44 1
 
0.1%
56 1
 
0.1%
57 2
0.3%
61 1
 
0.1%
62 1
 
0.1%
65 1
 
0.1%
67 1
 
0.1%
68 3
0.4%
71 4
0.5%
72 1
 
0.1%
ValueCountFrequency (%)
199 1
 
0.1%
198 1
 
0.1%
197 4
0.5%
196 3
0.4%
195 2
0.3%
194 3
0.4%
193 2
0.3%
191 1
 
0.1%
190 1
 
0.1%
189 4
0.5%

BloodPressure
Real number (ℝ)

Distinct47
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.405184
Minimum24
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-06-20T23:02:04.792480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile52
Q164
median72.202592
Q380
95-th percentile90
Maximum122
Range98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.096346
Coefficient of variation (CV)0.16706464
Kurtosis1.0977837
Mean72.405184
Median Absolute Deviation (MAD)7.7974079
Skewness0.13730537
Sum55607.181
Variance146.32159
MonotonicityNot monotonic
2025-06-20T23:02:04.868551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
70 57
 
7.4%
74 52
 
6.8%
78 45
 
5.9%
68 45
 
5.9%
72 44
 
5.7%
64 43
 
5.6%
80 40
 
5.2%
76 39
 
5.1%
60 37
 
4.8%
72.40518417 35
 
4.6%
Other values (37) 331
43.1%
ValueCountFrequency (%)
24 1
 
0.1%
30 2
 
0.3%
38 1
 
0.1%
40 1
 
0.1%
44 4
 
0.5%
46 2
 
0.3%
48 5
 
0.7%
50 13
1.7%
52 11
1.4%
54 11
1.4%
ValueCountFrequency (%)
122 1
 
0.1%
114 1
 
0.1%
110 3
0.4%
108 2
0.3%
106 3
0.4%
104 2
0.3%
102 1
 
0.1%
100 3
0.4%
98 3
0.4%
96 4
0.5%

SkinThickness
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.15342
Minimum7
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-06-20T23:02:04.942043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14.35
Q125
median29.15342
Q332
95-th percentile44
Maximum99
Range92
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.7909419
Coefficient of variation (CV)0.30154068
Kurtosis5.4148455
Mean29.15342
Median Absolute Deviation (MAD)3.8465804
Skewness0.82217314
Sum22389.826
Variance77.28066
MonotonicityNot monotonic
2025-06-20T23:02:05.011446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.15341959 227
29.6%
32 31
 
4.0%
30 27
 
3.5%
27 23
 
3.0%
23 22
 
2.9%
18 20
 
2.6%
33 20
 
2.6%
28 20
 
2.6%
31 19
 
2.5%
39 18
 
2.3%
Other values (41) 341
44.4%
ValueCountFrequency (%)
7 2
 
0.3%
8 2
 
0.3%
10 5
 
0.7%
11 6
0.8%
12 7
0.9%
13 11
1.4%
14 6
0.8%
15 14
1.8%
16 6
0.8%
17 14
1.8%
ValueCountFrequency (%)
99 1
 
0.1%
63 1
 
0.1%
60 1
 
0.1%
56 1
 
0.1%
54 2
0.3%
52 2
0.3%
51 1
 
0.1%
50 3
0.4%
49 3
0.4%
48 4
0.5%

Insulin
Real number (ℝ)

Distinct186
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.54822
Minimum14
Maximum846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-06-20T23:02:05.081374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile50
Q1121.5
median155.54822
Q3155.54822
95-th percentile293
Maximum846
Range832
Interquartile range (IQR)34.048223

Descriptive statistics

Standard deviation85.021108
Coefficient of variation (CV)0.54659003
Kurtosis15.185233
Mean155.54822
Median Absolute Deviation (MAD)3.5
Skewness3.0190837
Sum119461.04
Variance7228.5888
MonotonicityNot monotonic
2025-06-20T23:02:05.152059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
155.5482234 374
48.7%
105 11
 
1.4%
130 9
 
1.2%
140 9
 
1.2%
120 8
 
1.0%
94 7
 
0.9%
180 7
 
0.9%
100 7
 
0.9%
110 6
 
0.8%
115 6
 
0.8%
Other values (176) 324
42.2%
ValueCountFrequency (%)
14 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
18 2
0.3%
22 1
 
0.1%
23 2
0.3%
25 1
 
0.1%
29 1
 
0.1%
32 1
 
0.1%
36 3
0.4%
ValueCountFrequency (%)
846 1
0.1%
744 1
0.1%
680 1
0.1%
600 1
0.1%
579 1
0.1%
545 1
0.1%
543 1
0.1%
540 1
0.1%
510 1
0.1%
495 2
0.3%

BMI
Real number (ℝ)

High correlation 

Distinct248
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.457464
Minimum18.2
Maximum67.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-06-20T23:02:05.226231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.235
Q127.5
median32.4
Q336.6
95-th percentile44.395
Maximum67.1
Range48.9
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.8751513
Coefficient of variation (CV)0.21182035
Kurtosis0.91949026
Mean32.457464
Median Absolute Deviation (MAD)4.6
Skewness0.59825266
Sum24927.332
Variance47.267706
MonotonicityNot monotonic
2025-06-20T23:02:05.297255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 13
 
1.7%
31.6 12
 
1.6%
31.2 12
 
1.6%
32.45746367 11
 
1.4%
32.4 10
 
1.3%
33.3 10
 
1.3%
32.9 9
 
1.2%
30.1 9
 
1.2%
30.8 9
 
1.2%
32.8 9
 
1.2%
Other values (238) 664
86.5%
ValueCountFrequency (%)
18.2 3
0.4%
18.4 1
 
0.1%
19.1 1
 
0.1%
19.3 1
 
0.1%
19.4 1
 
0.1%
19.5 2
0.3%
19.6 3
0.4%
19.9 1
 
0.1%
20 1
 
0.1%
20.1 1
 
0.1%
ValueCountFrequency (%)
67.1 1
0.1%
59.4 1
0.1%
57.3 1
0.1%
55 1
0.1%
53.2 1
0.1%
52.9 1
0.1%
52.3 2
0.3%
50 1
0.1%
49.7 1
0.1%
49.6 1
0.1%

DiabetesPedigreeFunction
Real number (ℝ)

Distinct517
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4718763
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-06-20T23:02:05.366222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.14035
Q10.24375
median0.3725
Q30.62625
95-th percentile1.13285
Maximum2.42
Range2.342
Interquartile range (IQR)0.3825

Descriptive statistics

Standard deviation0.3313286
Coefficient of variation (CV)0.70215138
Kurtosis5.5949535
Mean0.4718763
Median Absolute Deviation (MAD)0.1675
Skewness1.9199111
Sum362.401
Variance0.10977864
MonotonicityNot monotonic
2025-06-20T23:02:05.437311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.258 6
 
0.8%
0.254 6
 
0.8%
0.207 5
 
0.7%
0.261 5
 
0.7%
0.259 5
 
0.7%
0.238 5
 
0.7%
0.268 5
 
0.7%
0.27 4
 
0.5%
0.263 4
 
0.5%
0.304 4
 
0.5%
Other values (507) 719
93.6%
ValueCountFrequency (%)
0.078 1
0.1%
0.084 1
0.1%
0.085 2
0.3%
0.088 2
0.3%
0.089 1
0.1%
0.092 1
0.1%
0.096 1
0.1%
0.1 1
0.1%
0.101 1
0.1%
0.102 1
0.1%
ValueCountFrequency (%)
2.42 1
0.1%
2.329 1
0.1%
2.288 1
0.1%
2.137 1
0.1%
1.893 1
0.1%
1.781 1
0.1%
1.731 1
0.1%
1.699 1
0.1%
1.698 1
0.1%
1.6 1
0.1%

Age
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.240885
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-06-20T23:02:05.513049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.760232
Coefficient of variation (CV)0.35378816
Kurtosis0.64315889
Mean33.240885
Median Absolute Deviation (MAD)7
Skewness1.1295967
Sum25529
Variance138.30305
MonotonicityNot monotonic
2025-06-20T23:02:05.592467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 72
 
9.4%
21 63
 
8.2%
25 48
 
6.2%
24 46
 
6.0%
23 38
 
4.9%
28 35
 
4.6%
26 33
 
4.3%
27 32
 
4.2%
29 29
 
3.8%
31 24
 
3.1%
Other values (42) 348
45.3%
ValueCountFrequency (%)
21 63
8.2%
22 72
9.4%
23 38
4.9%
24 46
6.0%
25 48
6.2%
26 33
4.3%
27 32
4.2%
28 35
4.6%
29 29
3.8%
30 21
 
2.7%
ValueCountFrequency (%)
81 1
 
0.1%
72 1
 
0.1%
70 1
 
0.1%
69 2
0.3%
68 1
 
0.1%
67 3
0.4%
66 4
0.5%
65 3
0.4%
64 1
 
0.1%
63 4
0.5%

Outcome
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size43.6 KiB
0
500 
1
268 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters768
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Length

2025-06-20T23:02:05.657587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-20T23:02:05.690708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring characters

ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Interactions

2025-06-20T23:02:03.758419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:00.149396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:00.656214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.169219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.642550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:02.092583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:02.814709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:03.258746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:03.827749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:00.215957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:00.723622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.233757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.697183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:02.149143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:02.876744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:03.322464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:03.900508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:00.286370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:00.784745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.296291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.759935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:02.208802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:02.934758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:03.385217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:03.968369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:00.349814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:00.853010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.356110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.815283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:02.264846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:02.991904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:03.442307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:04.028348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:00.409572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:00.912673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.414267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.869840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:02.605717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:03.043080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:03.498699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:04.097325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:00.473784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:00.974455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.467982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.923198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:02.654159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:03.098699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:03.553144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:04.154295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:00.531497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.034407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.523868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.975794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:02.705556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:03.148015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:03.613222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:04.222511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:00.594409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.104491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:01.579789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:02.033776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:02.759710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:03.205322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T23:02:03.681517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-20T23:02:05.728615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeBMIBloodPressureDiabetesPedigreeFunctionGlucoseInsulinOutcomePregnanciesSkinThickness
Age1.0000.1200.3640.0430.2810.1990.3140.5630.184
BMI0.1201.0000.2900.1340.2250.1720.3140.1220.545
BloodPressure0.3640.2901.0000.0080.2420.1070.1480.2750.204
DiabetesPedigreeFunction0.0430.1340.0081.0000.0900.0480.173-0.0200.051
Glucose0.2810.2250.2420.0901.0000.4020.4790.1800.186
Insulin0.1990.1720.1070.0480.4021.0000.2520.1400.190
Outcome0.3140.3140.1480.1730.4790.2521.0000.2510.213
Pregnancies0.5630.1220.275-0.0200.1800.1400.2511.0000.174
SkinThickness0.1840.5450.2040.0510.1860.1900.2130.1741.000

Missing values

2025-06-20T23:02:04.312741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-20T23:02:04.400377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
06.000000148.072.00000035.00000155.54822333.6000000.627501
11.00000085.066.00000029.00000155.54822326.6000000.351310
28.000000183.064.00000029.15342155.54822323.3000000.672321
31.00000089.066.00000023.0000094.00000028.1000000.167210
44.494673137.040.00000035.00000168.00000043.1000002.288331
55.000000116.074.00000029.15342155.54822325.6000000.201300
63.00000078.050.00000032.0000088.00000031.0000000.248261
710.000000115.072.40518429.15342155.54822335.3000000.134290
82.000000197.070.00000045.00000543.00000030.5000000.158531
98.000000125.096.00000029.15342155.54822332.4574640.232541
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
7581.0106.076.029.15342155.54822337.50.197260
7596.0190.092.029.15342155.54822335.50.278661
7602.088.058.026.0000016.00000028.40.766220
7619.0170.074.031.00000155.54822344.00.403431
7629.089.062.029.15342155.54822322.50.142330
76310.0101.076.048.00000180.00000032.90.171630
7642.0122.070.027.00000155.54822336.80.340270
7655.0121.072.023.00000112.00000026.20.245300
7661.0126.060.029.15342155.54822330.10.349471
7671.093.070.031.00000155.54822330.40.315230